Let \((\Omega , \mathcal {F}, \mathcal {P})\)  denote a probability space, where \(\Omega \)  is a sample space, \(\mathcal {F}\)  is a \(\sigma -\) algebra of subsets of \(\Omega \) , and \(\mathcal {P}\)  is a probability measure on \(\mathcal {F}\) .
A random variable (r.v.) is a map \(Y: \Omega \rightarrow \mathbb {R}\) .
We assume that the environment of the RL agent is described by a Markov decision process (MDP) [1]}.
